CN105738526A - Method for screening specific serum metabolism markers for triple-negative breast cancer - Google Patents

Abstract

The invention discloses a method for screening specific serum metabolic markers of triple-negative breast cancer. LC/MS instruments are used to analyze the serum samples of the experimental group A and the control group B respectively, and the peaks of substances in all samples are analyzed. Model discriminant analysis was performed on the response intensity data of the experimental group A and the control group B respectively, and on this basis, the model construction of PLS?DA and OPLS?DA was carried out, so as to obtain differentially expressed metabolites, screen and identify Biomarkers related to breast cancer occurrence and metastasis: lysolecithin, sphingomyelin and small molecular amino acids. The results are of great significance for elucidating the changes in the content of characteristic metabolites in the serum of patients with triple-negative breast cancer and the role of metabolites in the occurrence and development of tumors; at the same time, the application of this screening method can obtain effective targets for early diagnosis of breast cancer. And provide a data basis for establishing a specific cancer diagnosis model.

Description

Method for Screening Specific Serum Metabolic Markers of Triple Negative Breast Cancer

technical field

The present invention relates to a metabonomics method for overall evaluation of body metabolic changes caused by tumors and screening of valuable biomarkers, in particular to a method for screening specific serum metabolic markers of triple-negative breast cancer.

Background technique

Metabolomics is a technology developed in recent years, which is used to evaluate the changes of various small molecular compounds in biological samples under the stimulation of the environment or other factors. Early detection, early treatment, and personalized diagnosis and treatment of tumors are the development direction of tumor treatment. The emerging metabolomics has opened up new ideas for assisting early diagnosis, efficacy evaluation, and prognosis of tumors. The occurrence and development of tumors are accompanied by changes in the body's metabolites. Metabolomics can make an overall evaluation of the body's metabolic changes caused by tumors, and screen out valuable biomarkers for early diagnosis and treatment of tumors.

However, in the prior art, there is no method for screening biomarkers of body metabolic changes caused by triple-negative breast cancer.

Contents of the invention

Aiming at the above-mentioned deficiencies in the prior art, the present invention provides a method for screening specific serum metabolic markers of triple-negative breast cancer.

In order to solve the problems of the technologies described above, the present invention adopts the following technical solutions:

A method for screening triple-negative breast cancer-specific serum metabolic markers, the method comprising the steps of:

(1) Select a number of serums from female patients with triple-negative breast cancer as the experimental group A; select normal female serums equal in number to the experimental group A as the control group B;

(2) After thawing the serum of experimental group A and control group B at room temperature, accurately pipette 100 μL of serum samples into different 1.5mLEP tubes, use methanol to precipitate protein, and the ratio is sample:methanol=1:4, vortex for 30s, Centrifuge at 12,000 rpm for 15 minutes at 4°C, draw 200 μL of the supernatant, and transfer it to the sample injection vial for detection;

(3) The vials of different serum samples were respectively placed on the analysis platform of the LC-Q/TOF-MS experimental instrument for chromatographic separation and mass spectrometry detection;

Chromatographic separation conditions are: column temperature is 40°C, flow rate is 0.35mL/min; mobile phase composition: ① experimental group A: water + 0.1% formic acid; ② control group B: acetonitrile + 0.1% formic acid; injection volume is 4 μL , autosampler temperature 4°C;

Mass spectrometry conditions: ① Positive ion mode for detection conditions: Nitrogen as atomization and cone gas; flight tube detection mode V-type, capillary voltage 4kV, cone voltage 35kV, ion source temperature 100°C; desolvation temperature 350°C , Reverse cone airflow 50L/h, desolvation gas 600L/h, extraction cone 4V; ② Negative ion mode conditions: capillary voltage 3.5kV, cone voltage 50kV, ion source temperature 100°C; desolvation temperature 300°C, Reverse cone airflow 50L/h, desolvation gas 700L/h, extraction cone 4V; ion scanning time 0.03s, scanning interval 0.02s, data acquisition range: 50-1000m/z;

(4) Preprocess the LC/MS data obtained by the LC-Q/TOF-MS experimental instrument through the Mass Profiler software, and perform post-editing in the EXCEL 2010 software, and organize the final result into a two-dimensional data matrix, including variables, observed amount and peak intensity; all data were then normalized to the total signal integral;

(5) Import the edited data matrix into SIMCA-P software for principal component analysis, and obtain the cumulative ^R2X value and ^Q2 value of the principal component under the positive mode and the principal component under the negative mode, and the ^R2X value is the model's Explanation rate, ^Q2 value is the prediction rate of the model;

(6) Using supervised partial least squares discriminant analysis PLS-DA to analyze the two groups of samples, the model quality parameters are: principal components in positive mode and principal components in negative mode cumulative R ² X value, R ² Y value and Q ² value; R ² X value is the interpretation rate of the model, R ² Y value is the interpretation rate of the model, Q ² value is the prediction rate of the model;

(7) Further use the supervised method OPLS-DA for modeling analysis, and obtain the cumulative R ² X value, R ² Y value and Q ² value of the principal component in the positive mode and the principal component in the negative mode, and the R ² X value is Explanation rate of the model, R ² Y value is the interpretation rate of the model, Q ² value is the prediction rate of the model;

(8) Use the VIP value of the OPLS-DA model and combine the p-value of the t-test to find differentially expressed metabolites; the qualitative method of differentially expressed metabolites is: search the online database and compare the mass-to-charge ratio m/z of the mass spectrum Or accurate molecular mass mass; screen and identify biomarkers related to the occurrence and metastasis of triple-negative breast cancer: lysolecithin, sphingomyelin and small molecular amino acids.

As a preferred solution of the present invention, before importing the edited data matrix into SIMCA-P software for principal component analysis, the data group is normalized.

Compared with prior art, the present invention has following advantage:

1. Using high-throughput, high-sensitivity, and fast LC-Q/TOF-MS technology, the metabolomics analysis and comparison of serum from patients with triple-negative breast cancer and normal people is used to find the specificity of early diagnosis of breast cancer. Sexual serum metabolic markers. The results are of great significance for elucidating the changes of serum characteristic metabolites in patients with triple-negative breast cancer and the role of metabolites in the occurrence and development of tumors.

2. Applying this screening method can obtain effective targets for early diagnosis of breast cancer, and provide a data basis for establishing a specific cancer diagnosis model.

Description of drawings

Figure 1 is a single sample ion flow chromatogram of experimental group A (A: pos, B: neg);

Fig. 2 is the PCA score map (pos) of A-B and QC;

Fig. 3 is the PCA score map (neg) of A-B and QC;

Fig. 4 is the PLS-DA score figure (pos) of A-B two groups;

Fig. 5 is the PLS-DA sorting verification figure (pos) of Fig. 4 corresponding A-B two groups;

Fig. 6 is the PLS-DA score figure (neg) of A-B two groups;

Fig. 7 is the PLS-DA sorting verification figure (neg) of Fig. 6 corresponding A-B two groups;

Fig. 8 is the OPLS-DA score map (pos) of A-B two groups;

Figure 9 is the OPLS-DA score map (neg) of the A-B groups.

detailed description

The present invention will be described in further detail below in conjunction with the accompanying drawings and specific embodiments.

A method for screening triple-negative breast cancer-specific serum metabolic markers, the method comprising the steps of:

(1) A number of sera from female patients with triple-negative breast cancer were selected as the experimental group A; normal female sera equal to the number of the experimental group A were selected as the control group B. In this example, the serum of 31 female patients with triple-negative breast cancer was analyzed, and the serum of 31 normal females was used as a control.

(2) After thawing the serum of experimental group A and control group B at room temperature, accurately pipette 100 μL of serum samples into different 1.5mLEP tubes, use methanol to precipitate protein, and the ratio is sample:methanol=1:4, vortex for 30s, Centrifuge at 12,000 rpm for 15 minutes at 4°C, draw 200 μL of supernatant, and transfer it to a sample injection vial for detection. Methanol was HPLC grade methanol purchased from Merck (Dannstadt, Gennany).

(3) Place the vials of different serum samples on the analysis platform of the LC-Q/TOF-MS (Agilent, 1290InfinityLC, 6530UHD and Accurate-Mass Q-TOF/MS) experimental instrument for chromatographic separation and mass spectrometry detection; The column is a C ₁₈ chromatographic column (Agilent, 100 mm×2.1 mm, 1.8 μm).

Chromatographic separation conditions: column temperature 40°C, flow rate 0.35mL/min; mobile phase composition: ① experimental group A: water + 0.1% formic acid; ② control group B: acetonitrile + 0.1% formic acid; gradient elution procedure see Table 1. The injection volume was 4 μL, and the temperature of the autosampler was 4°C. Formic acid was purchased from CNW Company, and water was Watsons distilled water.

Table 1. The gradient of mobile phase

Mass spectrometry conditions: ① Positive ion mode is used for detection conditions: nitrogen is used as atomization and cone gas; flight tube detection mode is V-type. The positive ion mode conditions are: capillary voltage (capillary voltage) 4kV, cone voltage (Samplingcone) 35kV, ion source temperature (Source temperature) 100 ℃; desolvation temperature (Desolvation temperature) 350 ℃, reverse cone gas flow (Cone gas flow) 50L/h, Desolvation gasflow 600L/h, extraction cone 4V. ② Negative ion mode conditions: capillary voltage (capillary voltage) 3.5kV, cone voltage (Sampling cone) 50kV, ion source temperature (Source temperature) 100 ℃; desolvation temperature (Desolvation temperature) 300 ℃, reverse cone air flow (Cone gas flow) 50L/h, desolvation gas flow (Desolvation gas flow) 700L/h, extraction cone (Extraction cone) 4V; ion scan time (Scan time) 0.03s, scan time interval (Inter scan time) 0.02s, data Acquisition range: 50-1000m/z. In order to ensure the accuracy and repeatability of the mass, leucine-enkephalin was used as the lock mass (Lock mass), and [M+H]+ ion 556.2771Da was generated in the positive ion mode. [M-H]-ion 554.2615Da is produced in negative ion mode. By optimizing the conditions, the chromatograms of triple-negative breast cancer serum were obtained in positive ion mode and negative ion mode, respectively, as shown in Figure 1.

(4) Preprocess the LC/MS data obtained by the LC-Q/TOF-MS experimental instrument through the Mass Profiler software (Agilent Company), and perform post-editing in the EXCEL 2010 software, and organize the final result into a two-dimensional data matrix , including variable (rt_mz, ie retention time_mass-to-charge ratio), observation amount (sample) and peak intensity; samples get 1856 features in positive mode and 1004 features in negative mode. All data were then normalized to the total signal integral.

(5) Import the edited data matrix into SIMCA-P software (version 13.0) for principal component analysis, and obtain the cumulative R ² X value and Q ² value of the principal component in the positive mode and the principal component in the negative mode, R ² X The value is the explanation rate of the model, and the ^Q2 value is the prediction rate of the model.

The principal component analysis of the samples can reflect the overall metabolic difference between the two groups of samples and the variability of the samples within the group as a whole. Before formal analysis by SMICA-P software, the data set was normalized to obtain more intuitive and reliable results. In order to determine whether there is a difference between the two groups, the PCA modeling method was used to analyze the samples. In this analysis, a total of 9 principal components were obtained in the positive mode, and the cumulative R ² X = 0.379, Q ² = 0.0839; in the negative mode, a total of 9 principal components, cumulative R ² X =0.378, Q ² =0.0406. The PCA score plot (Scores plot) is shown in Figure 2 and Figure 3 . For the unsupervised model analysis of PCA, the main parameter to judge the quality of the model is R ² X, which represents the interpretation rate of the model. Generally speaking, a value greater than 0.4 indicates that the model is reliable. PCA analysis is an unsupervised model analysis method. Compared with supervised model analysis methods such as PLS-DA analysis, PCA more reliably reflects the most real differences between different groups.

(6) In order to obtain the metabolite information that caused this significant difference, a supervised multidimensional statistical method, Partial Least Squares Discriminant Analysis (PLS-DA), was used to analyze the two groups of samples, and the model quality parameters were: Positive The cumulative R ² X value, R ² Y value and Q ² value of the principal component in the mode and the principal component in the negative mode; the R ² X value is the explanation rate of the model, the R ² Y value is the explanation rate of the model, and the Q ² value is the predictive rate of the model.

In this embodiment, in positive mode, its model quality parameters are: with 3 principal components, R ² X = 0.209, R ² Y = 0.99, Q ² = 0.93; in negative mode with 4 principal components, R ² X = 0.208, R ² Y = 0.997, Q ² = 0.894. The main parameters to judge the quality of the model are R ² Y (the value represents the interpretation rate of the model) and Q ² value (the value is the prediction rate of the model), as shown in Figure 4, Figure 5, Figure 6 and Figure 7. Generally speaking, a value greater than 0.4 indicates that the model is reliable. In addition, the model will be sorted and verified to check whether the model is "overfitting". From the perspective of the parameters of the model, the model is reliable for explaining the differences between the two groups and finding the difference substances, and the model does not exist in the sorting verification diagram "Overfitting" phenomenon.

(7) Further use the supervised method OPLS-DA for modeling analysis, and obtain the cumulative R ² X value, R ² Y value and Q ² value of the principal component in the positive mode and the principal component in the negative mode, and the R ² X value is the interpretation rate of the model, R ² Y is the interpretation rate of the model, and Q ² is the prediction rate of the model.

In this example, 2 principal components and 1 orthogonal component are obtained in positive mode, R ² X = 0.209, R ² Y = 0.99, Q ² = 0.883; 2 principal components and 1 positive Intersection component, R ² X = 0.183, R ² Y = 0.984, Q ² = 0.833, the parameter R ² Y of the model represents the interpretation rate of the model, and Q ² represents the prediction rate of the model. Generally speaking, this parameter is greater than 0.4, indicating that this The model is reliable. Its scores are shown in Figures 8 and 9.

(8) Using the VIP (Variable Importance in the Projection) value of the OPLS-DA model (threshold > 1), combined with the p value of the t-test (p < 0.05) to find differentially expressed metabolites. The qualitative method of differential metabolites is: search the online database (Metlin), and compare the mass-to-charge ratio m/z or accurate molecular mass mass of the mass spectrum. The differential metabolite data are shown in Table 2 and Table 3. Screening and identification of biomarkers related to the occurrence and metastasis of triple-negative breast cancer are: lysolecithin, sphingomyelin and small molecular amino acids.

Table 2 Differential metabolites of A-B groups in positive mode

Table 2 Differential metabolites of A-B groups in negative mode

Fold change (A/B) is the logarithmic value (base 2) of the ratio of the mean value of group A to group B. A positive sign indicates that group A has increased relative to group B, and a negative sign indicates a decrease.

The present invention adopts LC/MS instrument to carry out metabonomics analysis on 62 cases of human serum samples respectively. Model discriminant analysis was performed on the response intensity data of the peaks of substances in all samples, and PCA analysis was performed on experiment A-control B respectively, and on this basis, the model construction of PLS-DA and OPLS-DA was carried out to obtain differential expression metabolism thing. Screen and identify biomarkers associated with breast cancer occurrence and metastasis. Among them, the majority of hemolytic lecithin and a small part of sphingomyelin have been confirmed to be disordered during the occurrence and development of tumors, and the metabolism of some small molecular amino acids is changed, which provides a basis for further searching for triple-negative breast cancer tumor markers, and is useful for exploring The occurrence and metastasis mechanism of triple-negative breast cancer and the choice of treatment measures point out the direction.

Finally, it is noted that the above embodiments are only used to illustrate the technical solutions of the present invention without limitation. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be carried out Modifications or equivalent replacements without departing from the spirit and scope of the technical solution of the present invention shall be covered by the claims of the present invention.

Claims (2)

1. the method screening three negative breast cancer specific serum metabolic markers, it is characterised in that the method includes walking as follows Rapid:

(1) multiple three negative breast cancer female patient serum are chosen as experimental group A；Choose equal just with experimental group A quantity Often women serum B as a control group；

(2), after the serum of thaw at RT experimental group A and matched group B, 100 μ L serum samples are the most precisely drawn different In 1.5mLEP pipe, using methanol extraction albumen, its ratio is sample: methanol=1:4, vortex 30s, carries out 4 DEG C, 12000rpm Centrifugal 15min, draws 200 μ L of supernatant liquid, proceeds in sample introduction bottle to be detected；

(3) bottle of different serum samples is individually positioned on the analysis platform of LC-Q/TOF-MS experimental apparatus carries out chromatograph Separate and Mass Spectrometer Method；

Chromatographic separation condition is: column temperature is 40 DEG C, flow velocity 0.35mL/min；Flowing phase composition: 1. experimental group A is: water+0.1% Formic acid；2. matched group B is: acetonitrile+0.1% formic acid；Sample size is 4 μ L, automatic sampler temperature 4 DEG C；

Mass Spectrometry Conditions: 1. use positive ion mode to carry out testing conditions: with nitrogen as atomization, taper hole gas；Tof tube detection mould Formula V type, capillary voltage 4kV, taper hole voltage 35kV, ion source temperature 100 DEG C；Desolventizing temperature 350 DEG C, reverse taper hole gas Stream 50L/h, desolventizing gas 600L/h, extraction taper hole 4V；2. negative ion mode condition: capillary voltage 3.5kV, taper hole voltage 50kV, ion source temperature 100 DEG C；Desolventizing temperature 300 DEG C, reverse taper hole air-flow 50L/h, desolventizing gas 700L/h, extraction Taper hole 4V；Ion scanning time 0.03s, trace interval 0.02s, data acquisition range: 50-1000m/z；

(4) by Mass Profiler software, the LC/MS data that LC-Q/TOF-MS experimental apparatus obtains are carried out pretreatment, and In EXCEL 2010 software, carry out later stage compilation, final result be organized as two-dimensional data matrix, including variable, observed quantity and Peak is strong；Then by all data normalizations to resultant signal integration；

(5) will editor after data matrix import SIMCA-P software carry out principal component analysis, it is thus achieved that the main constituent under holotype and Main constituent accumulation R under negative mode²X value and Q²Value, R²X value releases rate, Q for solution to model²Value is the prediction rate of model；

(6) using the offset minimum binary side discriminant analysis PLS-DA of supervision property to be analyzed two groups of samples, its model quality is joined Number is: the main constituent under holotype and the accumulation R of the main constituent under negative mode²X value, R²Y value and Q²Value；R²X value is released for solution to model Rate, R²Y value is that solution to model releases rate, Q²Value is the prediction rate of model；

(7) supervised method OPLS-DA is used further to be modeled analyzing, it is thus achieved that under main constituent under holotype and negative mode Main constituent accumulation R²X value, R²Y value and Q²Value, R²X value releases rate, R for solution to model²Y value is that solution to model releases rate, Q²Value is mould The prediction rate of type；

(8) use the VIP value of OPLS-DA model, and combine the p value of t-test to find differential expression metabolite；Diversity The qualitative method of metabolite is: search online database, relatively mass spectrographic mass-to-charge ratio m/z or accurate molecular quality mass；Sieve Select and identify to three negative breast carcinogenesis, the relevant biomarker of transfer and be: hemolytic lecithin, sphingomyelins and little molecule Aminoacid.

The method of screening three negative breast cancer specific serum metabolic markers the most according to claim 1, its feature exists In, before the data matrix importing SIMCA-P software after editor is carried out principal component analysis, data set is normalized.